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Results 11 - 20 of 74 for mat_mul (0.22 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_drq.mlir
// RUN: tf-quant-opt %s -split-input-file -quant-lift-quantizable-spots-as-functions -quant-prepare-quantize-drq -quant-quantize='weight-quantization=true' -verify-each=false | FileCheck %s // ----- module { func.func @matmul(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>) { %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 19:32:28 UTC 2024 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/remove_device_attribute.mlir
%0 = corert.get_op_handler %arg0 "cpu" // CHECK: %[[RESULT:.*]] = corert.executeop(%[[ARG_0:.*]]) "tf.MatMul"(%[[ARG_1:.*]], %[[ARG_1]]) {T = f32, transpose_a = false, transpose_b = false} : 1 %1 = corert.executeop(%0) "tf.MatMul"(%arg1, %arg1) {T = f32, device = "cpu", transpose_a = false, transpose_b = false} : 1 tfrt.return %arg0, %1 : !tfrt.chain, !corert.tensorhandle
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 25 10:58:25 UTC 2022 - 560 bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/unroll_batch_matmul.pbtxt
# RUN: tf_tfl_translate -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=2,5,3:3,7 -tf-input-data-types=DT_FLOAT,DT_FLOAT -tf-output-arrays=MatMul -unfold_batchmatmul=true -output-mlir %s -o - 2>&1 | FileCheck %s node { name: "Placeholder" op: "Placeholder" attr { key: "dtype" value { type: DT_FLOAT } } attr { key: "shape" value { shape { dim { size: 2 }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 2.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/propagate_quantize_type.mlir
// CHECK: %[[IDENTITY:.*]] = "tf.Identity"(%cst) : (tensor<2x1024xi8>) -> tensor<2x1024xi8> // CHECK: %[[MATMUL:.*]] = "tf.XlaDotV2"(%arg0, %[[IDENTITY]]) <{dimension_numbers = "\12\01\00\0A\01\03", precision_config = ""}> {device = ""} : (tensor<1x2x2x2xbf16>, tensor<2x1024xi8>) -> tensor<1x2x2x1024xbf16>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 6.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/fallback.mlir
// CHECK: tfrt_fallback_async.executeop key(2) cost({{.*}}) device("/device:CPU:0") "tf.MatMul" %0 = "tf.ReadVariableOp"(%arg1) {device = "/device:CPU:0", dtype = f32} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32> %1 = "tf.MatMul"(%arg0, %0) {T = f32, device = "/device:CPU:0", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 00:18:59 UTC 2024 - 9.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_quantizable_spots_as_functions_drq_min_elements.mlir
%cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<512x512xf32>} : () -> tensor<512x512xf32> %out_1 = "tf.MatMul"(%arg0, %cst) { device = "", transpose_a = false, transpose_b = false } : (tensor<1x12x12x512xf32>, tensor<512x512xf32>) -> tensor<*xf32> %out_2 = "tf.MatMul"(%arg0, %arg0) { device = "", transpose_a = false, transpose_b = true } : (tensor<1x12x12x512xf32>, tensor<1x12x12x512xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 2.1K bytes - Viewed (0) -
tensorflow/c/eager/c_api_remote_test.cc
ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_Op* matmul = MatMulOp(ctx, h0_task1, h1_task1); TFE_OpSetDevice(matmul, remote_device_name, status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_TensorHandle* retvals[1]; int num_retvals = 1; TFE_Execute(matmul, &retvals[0], &num_retvals, status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Aug 12 00:14:22 UTC 2020 - 5.4K bytes - Viewed (0) -
tensorflow/c/experimental/ops/README.md
category names correspond to generated source file names, and should be consistent with the original source files registering each operator. For example since `REGISTER_OP("MatMul")` appears in ***core/math_ops.cc***, the "MatMul" operator in the script should be in the "math" category, and it will be generated in the output file `c/experimental/ops/math_ops.cc`.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jul 28 17:21:01 UTC 2021 - 993 bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/mlrt/rewrite_ifrt_load_variable.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 22 21:35:32 UTC 2024 - 1.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/multi_variables_v1.py
# CHECK-NEXT: [[R1:%.*]] = "tf.ReadVariableOp"([[ARG1]]) {{{.*}}} : (tensor<!tf_type.resource<tensor<3x5xf32>>>) -> tensor<3x5xf32> # CHECK-NEXT: [[R2:%.*]] = "tf.MatMul"([[R0]], [[R1]]) <{{{.*}}}> {{{.*}}} : (tensor<5x3xf32>, tensor<3x5xf32>) -> tensor<5x5xf32> def Test(): x = tf.compat.v1.get_variable( name='x', shape=(5, 3),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 31 08:49:35 UTC 2023 - 2.6K bytes - Viewed (0)